These were all the libraries used to help with data exploration of the NBA data set.
library(dplyr)
library(ggplot2)
library(tidyr)
library(stringr)
library(data.table)
library(ggrepel)
library(directlabels)
library(gridExtra)
options(max.print = 999999999)
options(scipen=12)
## [1] "/Users/justinvhuang/Desktop/nba_stat_salaries"
## [1] "/Users/justinvhuang/Desktop/nba_stat_salaries"
The first thing that we should explore as a NBA team looking to have a succesful season in the modern era is to see the increased number of 3 point shots. How the NBA team according to many news articles has evolved into a spacing oriented and 3 point focused game during the regular season.
Let us first look at the distrubtion of the 3 point shot made, 2 point shot made and salary.
## median(salary) mean(salary) sd(salary) var(salary) IQR(salary)
## 1 2678400 4523495 4862128 23640286672352 4899590
There seems to be a right skew in the data. Showing that superstars and stars get the biggest pay day and take the biggest proportion of the teams salary.
## median(two) mean(two) sd(two) var(two) IQR(two)
## 1 2.1 2.667674 1.899015 3.60626 2.5
## median(three) mean(three) sd(three) var(three) IQR(three)
## 1 0.3 0.5867761 0.6674279 0.4454601 1
Both are right skewed showing that the stars and main players on the team usually take most of the shots.
Lets now look at the trend over the years of the three point shot vs two point shot.
The mean 3 point shots have been increasing indicated by the trending upward blue dots. While the number of 2 point shots have been decreasing by the red dots going down.
For players who took more than three 3’s a game there is an increase in salary after the 2015 year. However, there is still a decrease before then. Perhaps teams didn’t pay big salaries for 3 point specialist earlier on and emphasized on other basketball statistics.
Let us explore if 3 pointers lead to more wins
2014 seems to be an increase in wins for taking more than three 3’s a game. Before that the league was more big man focused with players such as Kevin Garnett, Tim Duncan, Shaquille O’neal, Jermaine O’neal, Dwight Howard.
Now let us explore the other end of the spectrum. With less than three 3’s a game.
There is still an increase in salary. This is due to the TV contract money with the NBA increasing the salary cap which was a huge factor. However, if you look at the summary of the mins and max you can see taking more than 3 threes a game is beneficial.
Attemping more 3’s is benefical it looks like. From the years 2004 to 2008 the league still had traditional big men who dominated the league. Spacing and rules didn’t emphasize spacing as much. Having players that take less than three 3’s a game leads to a team win that would be 8th seed in the east and out of the playoffs in the western conference.
Making more two’s of course leads to a better salary. The TV money seems have a major factor to increase the salary for the players after 2015.
Conclusion:
Overall 3 point shooting does lead to more wins and an increase of salary. However, due to the TV contract there was a huge salary bump as well as new CBA and talks with the players association.
With the increased reliance on data interpretation what we want to explore is the rise of EFG vs PPG. Players like Rudy Gay and Josh Smith who scored in bunches before used to be rewarded huge contracts. However, it was later found they scored inefficiently.
\[ EFG = (FG + 0.5 * 3P) / FGA. \]
Salary over the years has increased. But there doesn’t seem to be a clear relationship with points per game over the years and salary.
EFG seems to be trending in the later years. As more GMs and teams look at data they are paying more for players who are shooting more effectively. In the past teams perhaps were only looking at points per game.
Lets look at a 20 ppg scorer vs a 50 percent EFG players and see how their salaries compare.
Lets look at over 51 percent EFG
Early on EFG didn’t seem to take notice to teams as much, but as we entered the 2014 year and above this statisic became more relevant
Exploring this data could give us information on where to look to draft a NBA prospect for a NBA team.
## [1] "Alabama" "Alabama A&M"
## [3] "Alabama-Birmingham" "Alabama-Huntsville"
## [5] "American International" "Arizona"
## [7] "Arizona State" "Arkansas"
## [9] "Arkansas-Little Rock" "Auburn"
## [11] "Auburn-Montgomery" "Augsburg"
## [13] "Austin Peay" "Ball State"
## [15] "Barton Community College" "Baylor"
## [17] "Belmont" "Blinn"
## [19] "Boise State" "Boston College"
249 schools (no school included for internationals if they played professionally outside the USA)
Lets look at the top 10 schools that got paid
## # A tibble: 11 x 3
## school count_school salary
## <fct> <int> <dbl>
## 1 None 1285 7407695183
## 2 Kentucky 261 1220596079
## 3 Duke 252 1308479631
## 4 North Carolina 241 1182185814
## 5 UCLA 206 947159451
## 6 Kansas 194 840093830
## 7 Arizona 190 995023686
## 8 Connecticut 184 1038212650
## 9 Florida 143 817700613
## 10 Georgia Tech 129 718884989
## 11 Texas 116 627431018
Should be always looking at these schools for prospects looking at the total salary paid. Seems to have best basketball programs
Lets take a look at the data that was accounted for by college programs
Looking at the notable names Lebron James, Kobe Bryant, Kevin Garnett, Dwight Howard, Jermaine O’neal that would be 5 out of 430 highschoolers who made it to superstar level. That’s 1.2 percent chance.
Out of Country Prospects for teams to spend their salary on.
Looking at the top salaries would want to look into these countries in terms if finding talent or drafting in the future or free agents.
Now lets compare to being a relatively good team but paying for good scouts to draft or look abroad in the second round.
We can see that by not selecting the number one pick overall it yielded 4 more defensive players or nba team selections. But we have to keep in mind other picks in the first round. Other notable picks that aren’t first rounders include Stephen Curry, Kevin Durant, A’mare Stoudamire.
Lets look at the data for the winning team and team that lost the most for each year 2000 to 2018
Winning teams tend to spend over the cap while losing teams spend under or stay below the salary cap for each year.
Lets look at the Top teams each year vs Worst teams each year
We can see the top teams spend the most money each year. Their best players also recieve the more money than the worst teams player. Looking at the average salary the best teams spend much more than the worst.
Looking at the team that spent the most over and the team that spent the least.
Looking at the winningest team vs the team that lost the most in the last 19 years.
The team that won the most is the 2016 GSW and the team with the fewest wins was the 2012 Charlotte Bobcats. The team that spent the most over the salary cap was the 2018 Cleveland Caviliers. The team that spent the least over the salary cap was the 2000 LA clippers.
## # A tibble: 14 x 6
## # Groups: salary [14]
## name salary ppg EFG t_reb PlusMinus
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Klay Thompson 15501000 22.1 0.569 3.8 14.7
## 2 Draymond Green 14260870 14 0.554 9.5 18
## 3 Andrew Bogut 12000000 5.4 0.625 7 13.9
## 4 Andre Iguodala 11710456 7 0.544 4 13.2
## 5 Stephen Curry 11370786 30.1 0.631 5.4 17.7
## 6 Anderson Varejao 10158574 2.6 0.435 2.7 2.6
## 7 Shaun Livingston 5543725 6.3 0.531 2.2 7
## 8 Harrison Barnes 3873398 11.7 0.531 4.9 10.5
## 9 Marreese Speights 3815500 7.1 0.452 3.3 1.2
## 10 Leandro Barbosa 2500000 6.4 0.519 1.7 2.4
## 11 Festus Ezeli 2008748 7 0.54 5.6 14.6
## 12 Brandon Rush 1270964 4.2 0.542 2.5 -0.9
## 13 Ian Clark 947276 3.6 0.5 1 -7.3
## 14 James Michael McAdoo 845059 2.9 0.55 1.4 -15.9
## total TopPaid NameTopSal highscore NameTopPpg efficient
## 1 95806356 15501000 Klay Thompson 30.1 Stephen Curry 0.6311881
## NameTopEFG HighPlusMinus NameTopPM LeastPaid
## 1 Stephen Curry 18 Draymond Green 845059
## NameLowSal AvgSal tmsalary salcap OverUnder wins
## 1 James Michael McAdoo 6843311 93669566 70000000 1.338137 73
From the greenline we can see that team salary has stayed above salary cap.
Lets see how they drafted in the last 19 years
## name salary ppg EFG
## 104 Antawn Jamison 2503800 19.6 0.4715909
## 1564 Jason Richardson 2607360 15.6 0.4612676
## 1746 JR Bremer 563679 3.3 0.3295455
## 4732 Stephen Curry 2913840 18.6 0.5492958
## 5358 Klay Thompson 2222160 16.6 0.5102041
## 5813 Harrison Barnes 2923920 9.5 0.4482759
They also successfully drafted Draymond Green who won defensive player of the year.
Looking at the worst team in NBA history
## # A tibble: 14 x 6
## # Groups: salary [14]
## name salary ppg EFG t_reb PlusMinus
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Corey Maggette 10262069 15 0.402 3.9 -15.4
## 2 Tyrus Thomas 7305765 5.6 0.361 3.7 -14.4
## 3 DeSagana Diop 6925400 1.1 0.375 3.1 -19.1
## 4 Matt Carroll 3900000 2.7 0.367 1.1 -11.9
## 5 DJ Augustin 3236470 11.1 0.441 2.3 -14.7
## 6 Bismack Biyombo 2798040 5.2 0.455 5.8 -16
## 7 Eduardo Najera 2750000 2.6 0.448 2.3 -7.2
## 8 Reggie Williams 2500000 8.3 0.481 2.8 -18.4
## 9 Kemba Walker 2356320 12.1 0.414 3.5 -14.3
## 10 Gerald Henderson 2250600 15.1 0.466 4.1 -13.7
## 11 DJ White 2001167 6.8 0.492 3.6 -15.3
## 12 Byron Mullens 1288200 9.3 0.440 5 -12.4
## 13 Derrick Brown 854389 8.1 0.532 3.6 -13
## 14 Cory Higgins 473604 3.9 0.345 0.9 -12.9
## total TopPaid NameTopSal highscore NameTopPpg efficient
## 1 48902024 10262069 Corey Maggette 15.1 Gerald Henderson 0.531746
## NameTopEFG HighPlusMinus NameTopPM LeastPaid NameLowSal
## 1 Derrick Brown -7.2 Eduardo Najera 473604 Cory Higgins
## AvgSal tmsalary salcap OverUnder wins
## 1 3493002 57902024 58044000 0.997554 7
You can see Michael Jordan didn’t really spend that much money on his bobcats to have them winning.
Comparing the worst vs the best team side by side
The GSW looked as if they spent double the amount. But also have to include the salary cap increase. Teams with winning players will spend more over the cap than teams who can’t win. The real question is do teams spend just enough to stay in cap and then just tank to get draft picks and then players?
Looking at the max salary offender
## team PlayerYear
## 7508 CLE 2018
## 7509 CLE 2018
## 7510 CLE 2018
## 7514 CLE 2018
## 7520 CLE 2018
## 7538 CLE 2018
## 7539 CLE 2018
## 7552 CLE 2018
## 7772 CLE 2018
## 7773 CLE 2018
## 7774 CLE 2018
## 7775 CLE 2018
## 7776 CLE 2018
## 7777 CLE 2018
## 7778 CLE 2018
## total TopPaid NameTopSal highscore NameTopPpg efficient
## 1 138780646 33285709 LeBron James 27.5 LeBron James 0.7142857
## NameTopEFG HighPlusMinus NameTopPM LeastPaid NameLowSal AvgSal
## 1 Ante Zizic 7.9 Kyle Korver 77250 John Holland 9252043
## tmsalary salcap OverUnder wins
## 1 137722926 99093000 1.389835 50
## team PlayerYear
## 179 LAC 2000
## 180 LAC 2000
## 181 LAC 2000
## 182 LAC 2000
## 183 LAC 2000
## 184 LAC 2000
## 185 LAC 2000
## 186 LAC 2000
## 187 LAC 2000
## 217 LAC 2000
## 256 LAC 2000
## 258 LAC 2000
## 259 LAC 2000
## 281 LAC 2000
## 282 LAC 2000
## # A tibble: 15 x 6
## # Groups: salary [12]
## name salary ppg EFG t_reb PlusMinus
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Michael Olowokandi 3456240 9.8 0.432 8.2 -13.8
## 2 Tyrone Nesby 2716667 13.3 0.452 3.8 -13.2
## 3 Lamar Odom 2445480 16.6 0.467 7.8 -7.5
## 4 Eric Piatkowski 2000000 8.7 0.5 3 -12.6
## 5 Eric Murdock 1925000 5.6 0.431 1.9 -10.3
## 6 Keith Closs 1680000 4.2 0.486 3.1 -10.6
## 7 Derek Anderson 1439400 16.9 0.474 4 -13.1
## 8 Maurice Taylor 1367400 17.1 0.465 6.5 -12.8
## 9 Brian Skinner 843000 5.4 0.512 6.1 -6.6
## 10 Anthony Avent 510000 1.7 0.3 1.5 -8.3
## 11 Pete Chilcutt 510000 2.1 0.413 2.3 -12.1
## 12 Troy Hudson 460000 8.8 0.437 2.4 -10.9
## 13 Etdrick Bohannon 460000 2.2 0.5 2.4 -13.6
## 14 Jeff McInnis 460000 7.2 0.453 2.9 -13.3
## 15 Charles R Jones 385000 3.4 0.431 1.1 -12.1
## total TopPaid NameTopSal highscore NameTopPpg efficient
## 1 20658187 3456240 Michael Olowokandi 17.1 Maurice Taylor 0.5121951
## NameTopEFG HighPlusMinus NameTopPM LeastPaid NameLowSal
## 1 Brian Skinner -6.6 Brian Skinner 385000 Charles R Jones
## AvgSal tmsalary salcap OverUnder wins
## 1 1377212 22489343 34000000 0.6614513 15
## name salary ppg EFG
## 186 Michael Olowokandi 3456240 9.8 0.4315789
## 775 Lamar Odom 2628960 17.2 0.4963768
## 1059 Darius Miles 3054840 9.5 0.4871795
## 3942 Eric Gordon 2623200 16.1 0.5301724
## 3943 Al Thornton 1776240 16.8 0.4560811
## 4095 Eric Gordon 2819880 16.9 0.5277778
## 5085 Eric Bledsoe 1596360 3.3 0.4062500
## 5123 Blake Griffin 5731080 20.7 0.5483871
The Clippers drafted well with Blake Griffin however there were many injuries. So tanking to get a star was worth it. They also got Eric Gordon and Lamar Odom who both woth 6th man of the year.
Comparing them with the Cavs. We can see the Cavs spent money to keep their team intact for their Finals run.
## height weight salary plusMinus age games start
## 1 200.9356 222.0764 2880067 -1.1627685 27.74702 58.84248 28.57518
## 2 200.9545 222.6364 3398885 -1.2510101 27.72727 59.10859 29.96465
## 3 201.5609 224.6878 3487920 -0.9461929 27.18782 58.99239 29.94162
## 4 201.6292 224.7008 3783693 -0.9245524 27.12788 60.20716 30.38875
## 5 201.3472 224.0000 3817195 -0.9489637 27.13731 59.93782 30.67098
## 6 201.4734 224.2488 3911320 -1.5695652 26.94928 58.76570 29.57729
## 7 201.0049 223.2469 4091303 -1.2205379 26.54279 59.48166 30.03912
## 8 200.6675 222.7530 4140794 -1.2315914 26.44893 58.72922 29.19002
## 9 200.8610 222.3325 4574357 -1.1846154 26.84367 59.01985 29.46402
## 10 201.2897 223.0907 4715669 -1.4602015 26.57683 59.11839 30.14106
## 11 200.9824 222.8643 4829387 -1.1979899 26.63065 59.36181 29.98744
## 12 201.3957 224.0192 4560710 -1.2580336 26.63309 59.46043 29.47242
## 13 200.9750 222.8636 4348460 -1.7250000 26.58182 46.56591 22.44545
## 14 200.8822 222.1455 4447435 -1.1697460 26.74134 58.67206 28.35566
## 15 200.9120 221.4718 4402763 -1.0440181 26.56208 56.99097 27.74266
## 16 200.8978 221.3244 4430783 -1.5308889 26.68667 56.22222 26.72444
## 17 201.1982 221.5248 5097982 -1.4074324 26.71171 57.95045 27.69369
## 18 201.1186 220.4295 6218880 -1.3767338 26.44743 57.69128 27.50336
## 19 200.5735 218.2668 6450838 -1.6067227 26.18697 54.02731 25.83193
## mins fg fg_att fg_per three three_att three_per
## 1 21.36683 3.163962 7.130549 0.4365386 0.4152745 1.196897 0.3469591
## 2 22.04369 3.172980 7.226768 0.4314876 0.4270202 1.214646 0.3515593
## 3 22.13680 3.268528 7.395939 0.4361455 0.4573604 1.315990 0.3475410
## 4 21.92558 3.178517 7.233504 0.4312024 0.4498721 1.297954 0.3466010
## 5 22.43523 3.219689 7.393523 0.4314557 0.4694301 1.368135 0.3431168
## 6 22.23430 3.242995 7.316908 0.4368336 0.4985507 1.423430 0.3502461
## 7 22.09927 3.220049 7.142787 0.4449107 0.5051345 1.422005 0.3552270
## 8 21.78147 3.243468 7.123278 0.4489531 0.5399050 1.519952 0.3552118
## 9 21.88462 3.283623 7.260794 0.4452385 0.5727047 1.604963 0.3568336
## 10 22.30428 3.368766 7.394207 0.4500620 0.5881612 1.620907 0.3628594
## 11 22.20503 3.392211 7.397990 0.4549261 0.5753769 1.642714 0.3502600
## 12 21.86355 3.294005 7.226859 0.4536585 0.5693046 1.598801 0.3560822
## 13 21.37068 3.148636 7.088636 0.4374295 0.5572727 1.616364 0.3447694
## 14 21.42471 3.220785 7.195150 0.4430161 0.6217090 1.771824 0.3508863
## 15 21.31603 3.255305 7.228217 0.4441381 0.6706546 1.890745 0.3547039
## 16 21.10867 3.213556 7.233111 0.4399928 0.6842222 1.974444 0.3465391
## 17 21.10946 3.279054 7.300450 0.4472961 0.7204955 2.056081 0.3504217
## 18 20.98277 3.321477 7.314989 0.4507245 0.8192394 2.313423 0.3541244
## 19 20.86660 3.341807 7.363445 0.4518979 0.8915966 2.509244 0.3553248
## two two_att two_per efg ft ft_att ft_per
## 1 2.751551 5.931981 0.4533731 0.4636205 1.614797 2.168258 0.7447441
## 2 2.744192 6.006566 0.4447630 0.4589386 1.648737 2.220202 0.7426069
## 3 2.808376 6.078426 0.4528598 0.4633263 1.612183 2.154569 0.7482625
## 4 2.725064 5.936829 0.4467322 0.4589927 1.635806 2.171355 0.7533569
## 5 2.749223 6.030570 0.4465291 0.4599201 1.655181 2.217098 0.7465529
## 6 2.745652 5.896135 0.4558527 0.4670815 1.766184 2.355556 0.7497949
## 7 2.713692 5.719560 0.4639777 0.4762883 1.755990 2.367726 0.7416357
## 8 2.705226 5.605463 0.4710350 0.4836586 1.746793 2.325653 0.7510979
## 9 2.708189 5.654839 0.4662997 0.4822767 1.661042 2.228040 0.7455173
## 10 2.779597 5.770277 0.4695675 0.4879611 1.702519 2.234509 0.7619209
## 11 2.816834 5.754020 0.4830450 0.4916816 1.676131 2.223367 0.7538705
## 12 2.724460 5.628537 0.4743616 0.4914891 1.623501 2.148681 0.7555804
## 13 2.590455 5.472045 0.4634083 0.4749558 1.461364 1.953636 0.7480223
## 14 2.603233 5.422171 0.4720255 0.4849253 1.447575 1.937644 0.7470799
## 15 2.584424 5.334989 0.4844292 0.4899395 1.517156 2.014221 0.7532220
## 16 2.530889 5.258444 0.4724925 0.4866008 1.461333 1.960222 0.7454937
## 17 2.559910 5.245045 0.4820120 0.4949224 1.518919 2.022973 0.7508350
## 18 2.503803 5.000447 0.4925256 0.5044656 1.513199 1.970470 0.7679382
## 19 2.448950 4.857143 0.4964841 0.5103584 1.409874 1.842437 0.7652223
## o_reb d_reb t_reb assist steal block TO
## 1 1.1193317 2.684964 3.805489 1.926969 0.6947494 0.4441527 1.308115
## 2 1.1022727 2.782576 3.886616 1.927273 0.7131313 0.4750000 1.305303
## 3 1.1390863 2.761929 3.899239 1.955838 0.7104061 0.4794416 1.269543
## 4 1.1153453 2.731202 3.843223 1.909719 0.7150895 0.4659847 1.291816
## 5 1.1341969 2.789637 3.920466 1.946891 0.7300518 0.4608808 1.335233
## 6 1.1128019 2.739372 3.853382 1.914010 0.6917874 0.4492754 1.279710
## 7 1.0273839 2.715648 3.739120 1.831051 0.6562347 0.4283619 1.264792
## 8 1.0049881 2.682423 3.686698 1.876960 0.6539192 0.4121140 1.311639
## 9 1.0317618 2.776427 3.806700 1.949132 0.6625310 0.4344913 1.232506
## 10 1.0423174 2.806297 3.847355 1.875819 0.6690176 0.4632242 1.237028
## 11 1.0288945 2.821608 3.848995 1.907035 0.6600503 0.4555276 1.243970
## 12 1.0074341 2.775300 3.779376 1.876499 0.6565947 0.4510791 1.229976
## 13 1.0027273 2.702045 3.701364 1.835682 0.6779545 0.4477273 1.238182
## 14 0.9921478 2.727945 3.721247 1.934180 0.6866051 0.4533487 1.228176
## 15 0.9629797 2.783070 3.747178 1.915124 0.6717833 0.4164786 1.249661
## 16 0.9446667 2.802889 3.741111 1.905333 0.6786667 0.4097778 1.198889
## 17 0.9121622 2.896622 3.803153 1.929505 0.6826577 0.4344595 1.209459
## 18 0.8870246 2.876957 3.762640 1.930425 0.6657718 0.4105145 1.157271
## 19 0.8352941 2.870798 3.704202 1.993277 0.6739496 0.4115546 1.181513
## fouls ppg PlayerYear
## 1 2.126730 8.356086 2000
## 2 2.092424 8.421970 2001
## 3 1.990609 8.605330 2002
## 4 2.036829 8.443223 2003
## 5 2.034197 8.566062 2004
## 6 2.151691 8.751691 2005
## 7 2.141565 8.699022 2006
## 8 2.047743 8.775534 2007
## 9 1.960794 8.801737 2008
## 10 1.987406 9.029723 2009
## 11 1.983668 9.028141 2010
## 12 1.943405 8.773621 2011
## 13 1.779545 8.311364 2012
## 14 1.800231 8.513395 2013
## 15 1.869074 8.694808 2014
## 16 1.807778 8.568444 2015
## 17 1.811486 8.797523 2016
## 18 1.768680 8.970694 2017
## 19 1.753782 8.973319 2018
## name height weight salary plusMinus age games start
## 1 AvgJoe 201.0873 222.5623 4399392 -1.274556 26.81424 57.84977 28.6163
## mins fg fg_att fg_per three three_att three_per
## 1 21.7084 3.25418 7.261427 0.4429425 0.5806992 1.650448 0.3515404
## two two_att two_per efg ft ft_att ft_per
## 1 2.673354 5.61071 0.4679881 0.4806002 1.601489 2.132454 0.7511976
## o_reb d_reb t_reb assist steal block TO fouls
## 1 1.021201 2.775143 3.794608 1.912669 0.681629 0.4422839 1.251199 1.951981
## ppg
## 1 8.68851
Above is the chart of taking all the players and averaging them all out to create Mr. Avg Joe. Now lets compare him to differnt categories of NBA players.
What is suprising from the data is the height didn’t really change. However, perhaps the average height increased so there are more people 201cm in the league.
Lets see maybe the smallest player got taller and on average all nba players are the same height
Clearly there are still a lot of players who are 213 cm and over there was even a small increase in the year 2017.
SuperStars
Looking at the life cycle of a NBA player we can determine a salary reference point of how to pay them.
Duncan salary and production is above the average player. Late in his career he took pay cuts which brought his salary down.
Kobe commanded a much higher salary than the avg Joe. He also did not take a decrease in salary for the later years. However his minutes per game did not go down. It might be interesting to do an analysis on his achillies injury and overuse of his body in future analysis.
Lebron salary has kept going up. His body has not slowed down either. The amount of minutes played has not really decreased at all. Seems like a player anomally.
Garnett seems to have taken a few paycuts for the Timberwolves to get him better players. Production did go down a lot. Perhaps bigmen have smaller life cycles than smaller players.
Dirk took pay cuts as well. His production also started to decrease. It would be interesting to compare height and player life cycle.
Taller players that make it past year 10 seem to have a longer life cycle.
Stars
Vince Carters Life cycle goes up in years where it reaches its peak in his 30s then begins to decrease. Pay goes up because of new contract deals for NBA players increasing the overall salary cap.
Manu had a decrease in salary pay as he passed 35. But as soon as the contract money got signed the new TV deal he got paid due to an increase in salary cap overall.
There is no decreasein salary for Melo. However, this player is out of the league now due to the ineffciency.
Gasol also had a decrease in salary but the new TV money seems to have set in a new reference point on how much veterans should be paid.
Parkers salary went down but again due to the new TV contract money the salary did increase back up again even though the player was aging.
RolePlayer
Nene salary decreased. Probably got the veterans minimum with the new 2017 CBA.
Mike Miller took a pay cut probably to play for a contender.
Dunleavy had a pay decrease then a small increase because of the salary bump overall.
Jamal Crawford got a huge payday after the new TV money after an initial decrease.
Fisher never made it to the new TV contract money. His salary follows a normal trend according to his age.
MVP
## PlayerYear salary height_cm PlusMinus MinGames ppg EFG
## 37 2000 14000000 206 8.9 35.9 25.5 0.5111111
## 776 2001 19285715 216 8.7 39.5 28.7 0.5729167
## 972 2002 11250000 183 5.7 43.7 31.4 0.4226619
## 1243 2003 12072500 214 9.6 39.3 23.3 0.5145349
## 1639 2004 12676125 214 11.4 36.6 22.3 0.5029240
## 2267 2005 16000000 211 2.5 38.1 22.2 0.5030120
## 2772 2006 9625000 191 8.1 35.4 18.8 0.5783582
## 2952 2007 10500000 191 11.1 35.3 18.6 0.6132812
## 3488 2008 16360094 214 9.0 36.0 23.6 0.5087719
## 3741 2009 21262500 198 11.2 36.1 26.8 0.5023923
## 4106 2010 15779912 203 11.1 39.0 29.7 0.5447761
## 4569 2011 14500000 203 10.1 38.8 26.7 0.5425532
## 4859 2012 6993708 191 10.4 35.3 21.8 0.4719101
## 5527 2013 17545000 203 12.4 37.9 26.8 0.6067416
## 6107 2014 19067500 203 7.2 37.7 27.1 0.6107955
## 6413 2015 19997513 206 9.0 33.8 25.4 0.5780347
## 6635 2016 11370786 191 17.7 34.2 30.1 0.6311881
## 7091 2017 12112359 191 17.7 33.4 25.3 0.5765027
## 7785 2018 28299399 191 6.4 36.4 25.4 0.4786730
MVPs get more money than the average joe and they should.
## PlayerYear salary height_cm PlusMinus MinGames ppg EFG Type
## 370 2000 15004800 208 5.4 34.8 21.7 0.5533333 DPOY
## 542 2001 16880000 208 5.3 23.5 13.6 0.5185185 DPOY
## 884 2002 14315790 219 5.1 36.3 11.5 0.5000000 DPOY
## 1584 2003 5200000 206 4.2 39.4 6.9 0.4833333 DPOY
## 1640 2004 5500000 206 7.2 37.7 9.5 0.4239130 DPOY
## 2279 2005 6157895 201 6.7 41.6 24.6 0.5235294 DPOY
## 2504 2006 7500000 206 11.6 35.2 7.3 0.5087719 DPOY
## 3019 2007 16000000 206 3.0 35.0 6.4 0.4545455 DPOY
## 3440 2008 11250000 211 3.7 34.9 9.1 0.4562500 DPOY
## 3646 2009 24751934 211 13.6 31.1 15.8 0.5307692 DPOY
## 4197 2010 15202590 211 11.8 34.7 18.3 0.6078431 DPOY
## 4679 2011 16647180 211 8.8 37.6 22.9 0.5895522 DPOY
## 4978 2012 18091770 211 4.5 38.3 20.6 0.5746269 DPOY
## 5385 2013 13604188 216 6.1 32.8 10.4 0.6393443 DPOY
## 5849 2014 14860523 216 0.2 33.4 14.6 0.4710744 DPOY
## 6179 2015 12700000 211 2.1 30.6 7.2 0.4375000 DPOY
## 6983 2016 16407500 201 13.8 33.1 21.2 0.5695364 DPOY
## 7278 2017 17638063 201 8.8 33.4 25.5 0.5423729 DPOY
## 7764 2018 16400000 201 6.4 32.7 11.0 0.5170455 DPOY
Defensive player of the year doesn’t get as much as the MVP but still more than the average joe.
## PlayerYear salary height_cm PlusMinus MinGames ppg EFG
## 412 2000 2267280 201 3.2 38.1 25.7 0.4927536
## 786 2001 3629160 203 -8.0 39.3 20.1 0.4756098
## 827 2002 2494080 203 4.0 33.7 15.2 0.5118110
## 1363 2003 3193680 214 -2.2 36.0 19.0 0.5073529
## 1652 2004 1899720 208 -2.3 36.8 20.6 0.4777070
## 2172 2005 4320360 203 2.2 42.4 27.2 0.5023697
## 2413 2006 4020120 208 -4.2 33.6 13.2 0.4098361
## 3085 2007 3380160 183 -1.0 36.8 17.3 0.4705882
## 3504 2008 2883120 198 0.8 37.7 19.1 0.4873418
## 3686 2009 4484040 206 -8.6 39.0 25.3 0.5079787
## 4046 2010 5184480 191 -0.4 36.8 20.8 0.4943182
## 4742 2011 3880920 198 -4.5 37.0 17.8 0.4329268
## 5123 2012 5731080 208 7.5 36.2 20.7 0.5483871
## 5479 2013 5530080 191 -4.4 34.7 22.5 0.5027624
## 6101 2014 3202920 191 6.0 35.8 20.7 0.5062893
## 6583 2015 2300040 198 -3.6 32.6 14.6 0.4136691
## 6716 2016 5758680 203 -1.0 35.1 20.7 0.4781250
## 7178 2017 5960160 214 0.0 37.0 25.1 0.5777778
## 7861 2018 1312611 196 -2.4 29.9 13.0 0.5476190
Rookie salary took a dip after the new TV contract deal included in the 2017 CBA. Rookies get paid far less now compared to the average joe.
## PlayerYear salary height_cm PlusMinus MinGames ppg EFG
## 154 2000 4125000 186 1.0 31.6 16.2 0.4963235
## 431 2001 9660000 203 -0.3 40.9 20.5 0.4797688
## 1021 2002 10865250 203 5.2 38.3 25.6 0.4832536
## 1498 2003 6900000 211 3.5 37.2 20.8 0.4847561
## 1783 2004 8536000 191 -4.5 37.6 19.6 0.4608434
## 2394 2005 1504272 206 -2.9 34.8 18.9 0.4472050
## 2701 2006 8000000 198 1.3 33.8 13.4 0.5185185
## 2948 2007 1870501 203 5.1 31.1 9.7 0.5512821
## 3497 2008 770610 191 3.1 37.9 20.2 0.5331126
## 4308 2010 9930500 203 -0.1 36.7 24.1 0.4972826
## 4492 2011 2016692 183 -6.6 21.8 10.7 0.4343434
## 5110 2012 4609701 208 0.7 39.0 26.0 0.4948187
## 5360 2013 8700000 208 -5.1 30.9 16.2 0.5144928
## 6095 2014 3282003 206 6.7 36.2 21.7 0.4911765
## 6528 2015 7500000 191 -0.2 33.8 16.3 0.5468750
## 6625 2016 16407500 201 -0.1 36.9 20.9 0.4870130
## 7476 2017 3219579 191 1.0 35.0 23.0 0.5472222
## 7860 2018 22471911 211 3.1 36.7 26.9 0.5454545
MIP tends to get more money than the average Joe. Some years dipping below. Probably getting a pay increase after winning the award. Would be interesting to look at the trend.
MAX
## name PlayerYear salary height_cm PlusMinus MinGames ppg
## 7548 Stephen Curry 2018 34682550 191 13.2 32.0 26.4
## 90 LeBron James 2017 30963450 203 8.1 37.8 26.4
## 252 Kobe Bryant 2016 25000000 198 -14.8 28.2 17.6
## 15 Kobe Bryant 2015 23500000 198 -11.9 34.5 22.3
## 143 Kobe Bryant 2014 30453000 198 -7.4 29.5 13.8
## 9 Kobe Bryant 2013 27849000 198 2.0 38.6 27.3
## 219 Kobe Bryant 2012 25244493 198 3.0 38.5 27.9
## 315 Kobe Bryant 2011 24806250 198 7.5 33.9 25.3
## 244 Tracy McGrady 2010 23239562 203 -8.3 22.4 8.2
## 13 Kevin Garnett 2009 24751934 211 13.6 31.1 15.8
## EFG
## 7548 0.6213018
## 90 0.5906593
## 252 0.4142012
## 15 0.4093137
## 143 0.4467213
## 9 0.5073529
## 219 0.4630435
## 315 0.4850000
## 244 0.4166667
## 13 0.5307692
Max player obviously each year gets more for their production everyyear over the average joe.
Minimum Salary
To find which players qualified for the min salary. We took the salary for players that at least played 1 year in the NBA.
## PlayerYear salary height_cm PlusMinus MinGames ppg EFG
## 1 2018 592275.9 197.6429 -3.940179 14.71339 5.062500 0.4733439
## 2 2017 526671.2 198.3390 -3.752542 13.66780 4.613559 0.4810943
## 3 2016 486266.1 200.3704 -5.225926 13.48889 4.598148 0.4586663
## 4 2015 472604.7 199.0000 -5.264179 13.94627 4.628358 0.4556341
## 5 2014 496228.7 199.6533 -1.742667 13.88667 4.420000 0.4600698
## 6 2013 510376.4 198.9286 -3.623214 12.72321 4.033929 0.4647649
## 7 2012 464745.3 199.2982 -5.182456 12.74035 4.043860 0.4547385
## 8 2011 464942.7 199.0000 -4.469767 13.21163 4.683721 0.4710174
## 9 2010 454989.9 199.2927 -4.160976 14.30976 5.053659 0.4746463
## 10 2009 511001.2 198.8857 -4.068571 13.80857 4.260000 0.4809072
Min player of course is under the average joe player salary.
Mid Level Exception To see if a team could possibily sign a player went with 25 percent over and under the mid level exception everyyear. Also, did not want to have a player drafted that year as well as no player under 23 for that year.
## name ppg PlusMinus MinGames Games PlayerYear
## 6255 Klay Thompson 21.7 15.3 31.9 77 2015
## 6317 Damian Lillard 21.0 5.3 35.7 82 2015
## 6348 Danny Green 11.7 9.7 28.5 81 2015
## 6379 Marreese Speights 10.4 7.7 15.9 76 2015
## 6388 Matt Barnes 10.1 11.7 29.9 76 2015
## 6406 Anthony Morrow 10.7 5.5 24.4 74 2015
## 5821 Marco Belinelli 11.4 6.5 25.2 80 2014
## 5468 Vince Carter 13.4 5.5 25.8 81 2013
## 5529 Ty Lawson 16.7 5.4 34.4 73 2013
## 5612 JR Smith 18.1 5.7 33.5 80 2013
These are notable mid level exception players that fell into the category. Except for Damian Lilard and Klay most of these players could have possibly be signed to a mid level exception if they were free agents due to bad team management.
## PlayerYear salary height_cm PlusMinus MinGames ppg EFG
## 1 2018 5226548 199.8919 -1.4594595 19.97838 7.862162 0.5142168
## 2 2017 3392485 199.9000 -1.4300000 18.22250 7.077500 0.5026139
## 3 2016 3282897 201.0238 -1.2666667 18.58095 6.902381 0.4972249
## 4 2015 3267768 199.6735 -0.2469388 21.51633 8.748980 0.4935115
## 5 2014 3064909 202.5106 -0.6297872 19.58936 7.225532 0.5069429
## 6 2013 3080277 200.3958 -0.9916667 19.80833 7.329167 0.4928712
## 7 2012 3033485 202.2500 -1.4192308 21.43654 7.290385 0.4944566
## 8 2011 4746809 200.3542 -1.4395833 22.56667 8.477083 0.4938972
## 9 2010 5132612 200.6364 -0.3545455 22.12182 7.790909 0.4983103
## 10 2009 4924562 201.4000 -0.0320000 22.65800 7.588000 0.4923068
Mid level player used to get paid more but probably because of the previous CBA and building superteams they cut the amount so good maxed out teams cannot sign a good player with a huge mid level exception salary.